Top Data & Analytics Jobs in Las Vegas, NV

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

Lead and manage institutional research activities: collect, analyze, validate, and report quantitative and qualitative data; support program/course reviews, accreditation, dashboards, and decision-making; supervise a team and manage ad-hoc and ongoing reporting and assessment projects.

The Senior Analyst, Revenue Operations is responsible for delivering analytical insights, enhancing forecasting accuracy, and optimizing revenue processes while collaborating with various departments.

As a Senior Data Engineer, you will design and maintain data warehouses, ensuring data quality, manage data transformations, and mentor others while partnering with various stakeholders.

As a Data Analyst Intern, you will assist in data collection, cleaning, analysis, and visualization to support business decisions in a remote setup.

The VP of Data will lead the data organization encompassing engineering and analytics, drive strategic insights, improve operational excellence, and integrate AI into workflows while ensuring data governance.

As a Senior BI Data Analyst, you'll analyze complex datasets, build SQL queries, develop predictive models, and create dashboards to drive insights and recommendations.

The Sales Analytics Manager will oversee sales analytics, implement AI-driven reporting solutions, and partner with various teams to enhance data governance and performance forecasting.

The Senior Data Modeler designs and maintains data models, supports AI-driven practices, and collaborates on data lifecycle processes. Responsibilities include translating business needs into data structures, ensuring data governance, and supporting analytics and AI use cases.

The Data Ingestion Specialist will manage the ingestion of healthcare data, improve data models, debug issues, and collaborate with team members to maintain pipeline reliability and efficiency.